Path planning for mobile robots in complex environments based on improved ant colony algorithm
Aiming at the problems of the basic ant colony algorithm in path planning, such as long convergence time, poor global path quality and not being suitable for dynamic environments and unknown environments, this paper proposes a path planning method for mobile robots in complex environments based on a...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-07-01
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| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023695?viewType=HTML |
| _version_ | 1797739767992942592 |
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| author | Yuzhuo Shi Huijie Zhang Zhisheng Li Kun Hao Yonglei Liu Lu Zhao |
| author_facet | Yuzhuo Shi Huijie Zhang Zhisheng Li Kun Hao Yonglei Liu Lu Zhao |
| author_sort | Yuzhuo Shi |
| collection | DOAJ |
| description | Aiming at the problems of the basic ant colony algorithm in path planning, such as long convergence time, poor global path quality and not being suitable for dynamic environments and unknown environments, this paper proposes a path planning method for mobile robots in complex environments based on an improved ant colony (CBIACO) algorithm. First, a new probability transfer function is designed for an ant colony algorithm, the weights of each component in the function are adaptively adjusted to optimize the convergence speed of the algorithm, and the global path is re-optimized by using the detection and optimization mechanism of diagonal obstacles. Second, a new unknown environment path exploration strategy (UPES) is designed to solve the problem of poor path exploration ability of the ant colony algorithm in unknown environment. Finally, a collision classification model is proposed for a dynamic environment, and the corresponding dynamic obstacle avoidance strategy is given. The experimental results show that CBIACO algorithm can not only rapidly generate high-quality global paths in known environments but also enable mobile robots to reach the specified target points safely and quickly in a variety of unknown environments. The new dynamic obstacle avoidance strategy enables the mobile robot to avoid dynamic obstacles in different directions at a lower cost. |
| first_indexed | 2024-03-12T14:02:53Z |
| format | Article |
| id | doaj.art-91eb3702bfd64d48bb838b07fd6311bf |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-03-12T14:02:53Z |
| publishDate | 2023-07-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-91eb3702bfd64d48bb838b07fd6311bf2023-08-22T01:33:36ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-07-01209155681560210.3934/mbe.2023695Path planning for mobile robots in complex environments based on improved ant colony algorithmYuzhuo Shi 0Huijie Zhang1Zhisheng Li2Kun Hao 3Yonglei Liu 4Lu Zhao51. College of Information Technology, Tianjin College of Commerce, Tianjin 300350, China2. School of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300384, China2. School of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300384, China2. School of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300384, China2. School of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300384, China2. School of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300384, ChinaAiming at the problems of the basic ant colony algorithm in path planning, such as long convergence time, poor global path quality and not being suitable for dynamic environments and unknown environments, this paper proposes a path planning method for mobile robots in complex environments based on an improved ant colony (CBIACO) algorithm. First, a new probability transfer function is designed for an ant colony algorithm, the weights of each component in the function are adaptively adjusted to optimize the convergence speed of the algorithm, and the global path is re-optimized by using the detection and optimization mechanism of diagonal obstacles. Second, a new unknown environment path exploration strategy (UPES) is designed to solve the problem of poor path exploration ability of the ant colony algorithm in unknown environment. Finally, a collision classification model is proposed for a dynamic environment, and the corresponding dynamic obstacle avoidance strategy is given. The experimental results show that CBIACO algorithm can not only rapidly generate high-quality global paths in known environments but also enable mobile robots to reach the specified target points safely and quickly in a variety of unknown environments. The new dynamic obstacle avoidance strategy enables the mobile robot to avoid dynamic obstacles in different directions at a lower cost.https://www.aimspress.com/article/doi/10.3934/mbe.2023695?viewType=HTMLpath planningant colony algorithmunknown environmentpath explorationdynamic obstacle avoidance |
| spellingShingle | Yuzhuo Shi Huijie Zhang Zhisheng Li Kun Hao Yonglei Liu Lu Zhao Path planning for mobile robots in complex environments based on improved ant colony algorithm Mathematical Biosciences and Engineering path planning ant colony algorithm unknown environment path exploration dynamic obstacle avoidance |
| title | Path planning for mobile robots in complex environments based on improved ant colony algorithm |
| title_full | Path planning for mobile robots in complex environments based on improved ant colony algorithm |
| title_fullStr | Path planning for mobile robots in complex environments based on improved ant colony algorithm |
| title_full_unstemmed | Path planning for mobile robots in complex environments based on improved ant colony algorithm |
| title_short | Path planning for mobile robots in complex environments based on improved ant colony algorithm |
| title_sort | path planning for mobile robots in complex environments based on improved ant colony algorithm |
| topic | path planning ant colony algorithm unknown environment path exploration dynamic obstacle avoidance |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023695?viewType=HTML |
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